# A Dataset on Malicious Paper Bidding in Peer Review

arXiv: https://arxiv.org/abs/2207.02303 · HTML: https://mlsanity.com/paper/2207.02303/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 24 Jun 2022.

## Builds on (most cited first, 8 of 8)

- [Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments](https://mlsanity.com/paper/2006.16437/) (2020)
- [Design and Analysis of the NIPS 2016 Review Process](https://mlsanity.com/paper/1708.09794/) (2017)
- [Paper Matching with Local Fairness Constraints](https://mlsanity.com/paper/1905.11924/) (2019)
- [Matching Papers and Reviewers at Large Conferences](https://mlsanity.com/paper/2202.12273/) (2022)
- [On Strategyproof Conference Peer Review](https://mlsanity.com/paper/1806.06266/) (2018)
- [Making Paper Reviewing Robust to Bid Manipulation Attacks](https://mlsanity.com/paper/2102.06020/) (2021)
- [A SUPER* Algorithm to Optimize Paper Bidding in Peer Review](https://mlsanity.com/paper/2007.07079/) (2020)
- [Near-Optimal Reviewer Splitting in Two-Phase Paper Reviewing and Conference Experiment Design](https://mlsanity.com/paper/2108.06371/) (2021)

## Built on by (most cited first, 9 of 9)

- [ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing](https://mlsanity.com/paper/2306.00622/) (2023)
- [What Can Natural Language Processing Do for Peer Review?](https://mlsanity.com/paper/2405.06563/) (2024)
- [A Gold Standard Dataset for the Reviewer Assignment Problem](https://mlsanity.com/paper/2303.16750/) (2023)
- [Insights from the ICLR Peer Review and Rebuttal Process](https://mlsanity.com/paper/2511.15462/) (2025)
- [Automatic Evaluation Metrics for Artificially Generated Scientific Research](https://mlsanity.com/paper/2503.05712/) (2025)
- [On the Detection of Reviewer-Author Collusion Rings From Paper Bidding](https://mlsanity.com/paper/2402.07860/) (2024)
- [Vulnerability of Text-Matching in ML/AI Conference Reviewer Assignments to Collusions](https://mlsanity.com/paper/2412.06606/) (2024)
- [Tradeoffs in Preventing Manipulation in Paper Bidding for Reviewer Assignment](https://mlsanity.com/paper/2207.11315/) (2022)
- [CABAL: Multi-Agent Simulacra for Tracing the Effects of Collusive Bidding in Peer Review](https://mlsanity.com/paper/2609.05227/) (2026)

## Abstract (arXiv)

In conference peer review, reviewers are often asked to provide "bids" on each submitted paper that express their interest in reviewing that paper. A paper assignment algorithm then uses these bids (along with other data) to compute a high-quality assignment of reviewers to papers. However, this process has been exploited by malicious reviewers who strategically bid in order to unethically manipulate the paper assignment, crucially undermining the peer review process. For example, these reviewers may aim to get assigned to a friend's paper as part of a quid-pro-quo deal. A critical impediment towards creating and evaluating methods to mitigate this issue is the lack of any publicly-available data on malicious paper bidding. In this work, we collect and publicly release a novel dataset to fill this gap, collected from a mock conference activity where participants were instructed to bid either honestly or maliciously. We further provide a descriptive analysis of the bidding behavior, including our categorization of different strategies employed by participants. Finally, we evaluate the ability of each strategy to manipulate the assignment, and also evaluate the performance of some simple algorithms meant to detect malicious bidding. The performance of these detection algorithms can be taken as a baseline for future research on detecting malicious bidding.